Real world business process models may consist of hundreds of elements and have sophisticated structure. Although there are tasks where such models are valuable and appreciated, in general complexity has a negative influence on model comprehension and analysis. Thus, means for managing the complexity of process models are needed. One approach is abstraction of business process models—creation of a process model which preserves the main features of the initial elaborate process model, but leaves out insignificant details. In this paper we study the structural aspects of process model abstraction and introduce an abstraction approach based on process structure trees (PST). The developed approach assures that the abstracted process model preserves the ordering constraints of the initial model. It surpasses pattern-based process model abstraction approaches, allowing to handle graph-structured process models of arbitrary structure. We also provide an evaluation of the proposed approach.

1. On Application of Structural Decomposition for Process Model Abstraction Artem Polyvyanyy Sergey Smirnov Mathias Weske BPSC 2009 24 March 2009

6. Aggregation vs. Elimination Aggregate Eliminate

7. Assumption: Sound Process Models Hidden deadlock Hidden unsafe process fragment unsound model sound model Assume initial models to be sound

8. Stepwise Abstraction

9. Order Preserving Abstraction F A A and B belong to F A , ordering constraints are lost C and D do not belong to F A , ordering constraints are preserved A belong to F A and D does not, ordering constraints between F and D as between A and D

12. Relations between SESE Fragments p arent - child predecessor-successor if the node set of SESE fragment f 1 is the subset of node set of SESE fragment f 2 , then f 1 is the child of f 2 and f 2 is the parent of f 1 SESE fragment f 1 precedes SESE fragment f 2 (and f 2 succeeds f 1 ) if the outgoing edge of f 1 is the incoming edge of f 2 P 1 c 2 c 1 p 1 s 2 s 1 p 2

13. Process Structure Tree parent-child predecessor-successor

16. Abstraction Smoothness smoothness = 2 smoothness = 2 smoothness = 5 … loss of information is essential and desired … abstraction smoothness quantitatively estimates the information loss produced by one abstraction step

18. Smoothness Evaluation (II) „ Optimistic“ algorithm „ Pessimistic“ algorithm